Springer Simulation-Based Algorithms for Markov Decision Processes (Communications and Control Engineering)

The updated 2nd edition of this book covers MDPs in constrained settings and with uncertain transition properties; approximation stochastic annealing, a population-based on-line simulation-based algorithm; game-theoretic method for solving MDPs and more. Review From the book reviews:“The book consists of five chapters. … This well-written book is addressed to researchers in MDPs and applied modeling with an interests in numerical computations, but the book is also accessible to graduate students in operation research, computer science, and economics. The authors gives many pseudocodes of algorithms, numerical examples, algorithms convergence analysis and bibliographical notes that can be very helpful for readers to understand the ideas presented in the book and to perform experiments on their own.” (Wiesław Kotarski, zbMATH, Vol. 1293, 2014) From the Back Cover Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.  Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable.  In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function.  Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: . innovative material on MDPs, both in constrained settings and with uncertain transition properties; . game-theoretic method for solving MDPs; . theories for developing roll-out based algorithms; and . details of approximation stochastic annealing, a population-based on-line simulation-based algorithm.The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research. The Communications and Control Engineering series reports major technological advances which have potential for great impact in the fields of communication and control. It reflects research in industrial and academic institutions around the world so that the readership can exploit new possibilities as they become available. About the Author Hyeong Soo Chang (SM’07 of the IEEE, Member of INFORMS) received the B.S. and M.S. degrees in electrical engineering and the Ph.D. degree in electrical and computer engineering, all from Purdue University,West Lafayette, IN, in 1994, 1996, and 2001, respectively. Since 2003, he has been with the Department of Computer Science and Engineering, Sogang University, Seoul, Korea, where he is now an Associate Professor. He has about 30 journal papers in the area of MDPs and related areas. His main research interests include Markov decision processes, Markov games, computational learning theory, computational intelligence, and stochastic optimization.  He currently serves as an Associate Editor for the IEEE Transactions on Automatic Control. Jiaqiao Hu (M’11 of the IEEE, Member of INFORMS) received the B.S. degree in automation from Shanghai Jiao Tong University, Shanghai, China, in 1997, the M.S. degree in applied mathematics from the University of Maryland, Baltimore County, in 2001,